{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/restoring-extremely-dark-images-in-real-time","title":"Restoring Extremely Dark Images in Real Time","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Mohit Lamba","Kaushik Mitra"],"abstract":"    A practical low-light enhancement solution must be computationally fast, memory-efficient, and achieve a visually appealing restoration. Most of the existing methods target restoration quality and thus compromise on speed and memory requirements, raising concerns about their real-world deployability. We propose a new deep learning architecture for extreme low-light single image restoration, which is exceptionally lightweight, remarkably fast, and produces a restoration that is perceptually at par with state-of-the-art computationally intense models. To achieve this, we do most of the processing in the higher scale-spaces, skipping the intermediate-scales wherever possible. Also unique to our model is the potential to process all the scale-spaces concurrently, offering an additional 30% speedup without compromising the restoration quality. Pre-amplification of the dark raw-image is an important step in extreme low-light image enhancement. Most of the existing state-of-the-art methods need GT exposure value to estimate the pre-amplification factor, which is not practically feasible. Thus, we propose an amplifier module that estimates the amplification factor using only the input raw image and can be used \"off-the-shelf\"\" with pre-trained models without any fine-tuning. We show that our model can restore an ultra-high-definition 4K resolution image in just 1sec on a CPU and at 32fps on a GPU and yet maintain a competitive restoration quality. We also show that our proposed model, without any fine-tuning, generalizes well to cameras not seen during training and to subsequent tasks such as object detection.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2021/html/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2021/papers/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"restoring-extremely-dark-images-in-real-time","repo_url":"https://github.com/MohitLamba94/Restoring-Extremely-Dark-Images-In-Real-Time","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}